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Introduction The health and well-being of practicing American surgeons has become the focus of several recent studies (2008 ACS Survey). The studies have addressed quality of life and practice, burnout, depression, suicidal ideation, and medical errors. However, the prevalence of substance use disorders and their relationship to these other outcome measures were not incorporated into those studies. In 2010, the ACS BG Committee on Physician Competency and Health in conjunction with the Mayo Clinic Survey Center performed a survey in which data was collected for substance use, misuse, abuse, and dependence.

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General Population Prevalence of substance use disorders (SUDS) in the United States has been well documented: 9.4% of the US population > age 12 meet current criteria for substance abuse or dependence. Males are twice as likely as females to have met the criteria for alcohol dependence or abuse in the past year (10.5 vs. 5.1 percent). Consistent across demographic groups.

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SUDS Data on Physicians Actual period prevalence and point prevalence for physicians is unknown. Data comes from licensing boards, mortality studies, hospital statistics, treatment programs, surveys of training programs, medical and professional societies, and treated doctors. Prevalence of substance use disorders among physicians in the U.S. has been estimated to be 10% to 15%. Because of prescribing authority and access to high potency opioids, physicians are more likely to abuse prescribed opioids and benzodiazepines than the general population.

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2010 Methodology 1 Study eligibility and administration process were identical to the previous 2008 American College of Surgeons (ACS) study. All surgeons who were members of the ACS, had an e-mail address on file with the College, and permitted their e-mail to be used for correspondence with the College, were eligible for participation in this study. Participation was voluntary and all responses were anonymous. The study was commissioned by the ACS Committee on Physician Competency and Health (PCH) with Institutional Review Board (IRB) oversight with respect to protection of human subjects by the Mayo Clinic IRB.

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Methodology 2 Abbreviated Maslach Burnout Inventory Two Question PRIME-MD for Depression Personal and Professional QOL with Likert Scales Alcohol Use Disorders Identification Test of the WHO (AUDIT-C)

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AUDIT Consumption Questions (AUDIT-C) 1. How often did you have a drink containing alcohol in the past year? Never (0) Monthly or less (1) Two to four times per month (2) Two to three times per week (3) Four or more times per week (4) 2. On days in the past year when you drank alcohol how many drinks did you typically drink? 1 or 2 (0) 3 or 4 (1) 5 to 6 (2) 7 to 9 (3) 10 or more (4) 3. How often do you have 6 or more drinks on an occasion in the past year? Never (0) Less than Monthly (1) Monthly (2) Weekly (3) Daily or almost daily (4)

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Alcohol Abuse or Dependence The minimum AUDIT C score (for non-drinkers) is 0 and the maximum possible score is 12. Consider a screen positive for Unhealthy Alcohol Use if AUDIT-C score is >/= 4 points for men or >/= 3 points for women Consider alcohol abuse or dependence if score >/=5 for men or >/=4 for women

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Results Of 24,372 surgeons sampled, 7164 surgeons (29.4%) completed the survey. 1112 surgeons (15.5%) had an AUDIT-C score consistent with alcohol abuse or dependence. The point prevalence for alcohol abuse or dependence in male surgeons was 13.9% and for female surgeons was 25.6%.

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Conclusions Surgeons suffer from alcohol abuse or dependence at a rate higher than the general population. The presence of alcohol abuse/dependence among surgeons has a strong association with several personal (age, gender, relationship status, relationship satisfaction, having children) and professional characteristics (years in practice, hours worked, nights on call). The presence of alcohol abuse/dependence among surgeons has a strong association with burnout, depression, suicidal ideation, quality of life, and recent medical errors.

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Conclusions Support of this study by the American College of Surgeons should serve as an example of leadership in identifying members that need help. These findings should decrease the shame and stigma associated with alcohol abuse or dependence and encourage surgeons to pursue treatment and rehabilitation to promote patient safety and personal well-being.

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Response Since Publication 5700 downloads first 8 weeks Credibility factor already 4.5 Gave 10 interviews (Reuters, etc.) “15% of U.S. surgeons report drinking problems” “A new survey conducted by Michael Oreskovich, MD of the University of Washington showed that more than 15 out of every 100 people getting surgery should be worried about this very thing” “The next mandatory operating room equiptment? How about a breathlyzer?”

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Sensitivitymeasures the proportion of actual positives which are correctly identified as such (e.g. the percentage of sick people who are correctly identified as having the condition). Specificity measures the proportion of negatives which are correctly identified (e.g. the percentage of healthy people who are correctly identified as not having the condition). These two measures are closely related to the concepts of type I and type II errors. A perfect predictor would be described as 100% sensitivity (i.e. predict all people from the sick group as sick) and 100% specificity (i.e. not predict anyone from the healthy group as sick), however theoretically any predictor will possess a minimum error bound known as the Bayes error rate.measures the proportion of actual positives which are correctly identified as such (e.g. the percentage of sick people who are correctly identified as having the condition). Specificity measures the proportion of negatives which are correctly identified (e.g. the percentage of healthy people who are correctly identified as not having the condition). These two measures are closely related to the concepts of type I and type II errors. A perfect predictor would be described as 100% sensitivity (i.e. predict all people from the sick group as sick) and 100% specificity (i.e. not predict anyone from the healthy group as sick), however theoretically any predictor will possess a minimum error bound known as the Bayes error rate.